eChapters

1.1 Introduction

Increased emission of greenhouse gases results in climate change which becomes one of the most prominent environmental issues to the world today. Carbon dioxide (CO2) is the major greenhouse gas whereas nitrous oxide (N2O) and methane (CH4) are other substantial contributors to climate change (IPCC, 2014, Tongwane et al., 2016). However, these gases are emitted in minimal quantities in comparison to carbon dioxide but impart more global warming effect by absorbing more infrared radiations (Oertel et al., 2016). The warming potential of one kg of CH4 is 23 times more than one kg of CO2 while  in the case of N2O, it was nearly 300 times more than CO2 (Ramaswamy et al., 2001, Bharti et al., 2019; Biswas et al., 2010). Around one-third of methane and two –thirds of nitrous oxide come from soil (Prather et al., 1995, Tubiello et al., 2015, Smith, 2016).

Key words : Glaciers, Glacial Lakes, Glacial Lake Outburst Floods (GLOF), Himachal Pradesh, Glacial Inventory, Glacier Recession.

Introduction

Glaciers are large masses of snow, re-crystallized ice and rock debris that accumulate in great quantities and begin to flow outwards and downwards under the pressure of their own weight. Glaciers form when yearly snowfall in a region far exceeds the amount of snow and ice that melts in a given summer. In this way, massive quantities of glacial material accumulate in relatively small periods of geologic time. The glaciers exist in environments of high and low precipitation and in many temperature regimes; they are found on all the continents except Australia and they span the globe from high altitudes in equatorial regions to the polar ice caps. Presently, 10% of the Earth’s land area (5.8 million square miles) is covered with glaciers storing about 75% of the world’s freshwater. However, during the last Ice Age, glaciers covered 32% of the total land area of the planet.

Key words : Integrated Watershed Management (IWM), GIS, DTM, Watershed Delination, Water Quality Modeling, RS, DSS, Ground Water Information System (GWIS).

Introduction

The availability of land and water resources has a major effect on the economic and ecological aspects of life everywhere around the world. As a result, the need to develop better tools for efficient land and water resources management has become very vital. For quicker, reliable and precise results, it is necessary to use the geospatial tools and techniques such as Global Positioning Systems (GPS), Geographic Information Systems (GIS) and Remote Sensing (RS) image interpretation and analysis techniques to manage the natural resources. The application of these tools and techniques not only facilitates timely and repetitive analysis of all the topological features, but also assists in development of Decision Support Systems (DSS) to generate alternative scenarios for optimal use of the natural resources in the long term. The optimal use of these resources through integrated management of land water and vegetation leads to enhanced production and productivity of food, fodder, fiber, fuel, fish, flower and forest resources. Moreover, the holistic management of the resources to produce these outputs is plausible and manageable within the watershed systems, which are hydro geological units from which the runoff resulting from precipitation flows past through a single outlet to downstream locations. The systematic management of land, water, vegetation and livestock resources within the watershed systems is termed as Integrated Watershed Management (IWM). Broadly, IWM can be defined as a multidisciplinary, holistic way of protecting and managing a watershed’s natural resources to enhance biomass production in an eco-friendly manner. The watershed is viewed as a hydro-geological complex and dynamic ecosystem in which natural and anthropogenic processes occur and interact resulting in generation of runoff at the watershed outlet.

Introduction

A geographic information system or geographical information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics. What goes beyond a GIS is a spatial data infrastructure, a concept that has no such restrictive boundaries. In a general sense, the term describes any information system that integrates stores, edits, analyzes, shares, and displays geographic information. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. Geographic information science is the science underlying geographic concepts, applications, and systems.

GIS is a broad term that can refer to a number of different technologies, processes, and methods. It is attached to many operations and has many applications related to engineering, planning, management, transport/logistics, insurance, telecommunications, and business. For that reason, GIS and location intelligence applications can be the foundation for many location-enabled services that rely on analysis and visualization. GIS can relate unrelated information by using location as the key index variable. Locations or extents in the Earth space–time may be recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a “real” physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.

12.1 Introduction
Having explored the dynamic realm of Numerical Weather Prediction, which focuses on forecasting the atmosphere’s state over days to weeks, we now shift our focus to a broader, longer-term perspective: Climate Data and Analysis. While weather describes the atmospheric conditions at a specific place and time, climate refers to the long-term average pattern of weather in a region, typically spanning decades or longer. Understanding and analyzing climate data is paramount for comprehending our planet’s past climate, identifying ongoing changes, and projecting future scenarios in a warming world. The study of climate is fundamentally data-driven. Unlike the instantaneous snapshots used in weather forecasting, climate analysis requires vast historical records, often extending back centuries or even millennia, to discern natural variability from human-induced changes. This data comes from a diverse array of sources, including instrumental records (thermometers, rain gauges, barometers), satellite observations, and proxy records (tree rings, ice cores, sediment layers) that provide indirect evidence of past climate conditions. The sheer volume and heterogeneous nature of this data present unique challenges and opportunities for analysis.
This chapter will delve into the essential aspects of climate data and the methodologies used to analyze it. We will begin by exploring the various types of climate data available, understanding their strengths, limitations, and how they are collected and curated. Crucially, we will examine how this data is used to characterize climate variability – the natural fluctuations within the climate system – and to detect climate trends, particularly those associated with global warming. We will introduce key climate indices that summarize large-scale atmospheric and oceanic patterns, providing simplified metrics for
understanding complex interactions.

2.1. Introduction

Water plays a pivotal role in this earth system. The withdrawal of water for irrigation is about 70% of the global freshwater. Water use efficiency is less than 50% in many countries. It has been forecasted by the Food and Agricultural Organization (FAO, 2015) that global water requirements will be increased by 50% by the year 2050 to meet the demands of agricultural water use. This is so because food demand will be higher for the growing human population. We have entered into an earth system where everything living has evolved and developed (Cosgrove and Loucks, 2015). There has been a new geological scenario, some say Anthropocene (Crutzen, 2002; Williams et al., 2011). Consequently, freshwater will increasingly be a scarce and the costliest resource in the future, which needs to be managed properly.

The global economy has adversely been affected to a considerable extent due to weather related disasters like droughts, floods, heat waves and cold waves which are not uncommon in the recent past due to global warming and climate change. For the first time, the year 1998 was declared as the weather related disastrous year globally. The year 2015 was the warmest year globally, again. Such weather phenomenon of warming more and more year after year was noticed in the current decade as well when we look at 2023 and 2024. The year 2010 was the warmest year ever recorded, followed by 2009 in India. The occurrence of floods and droughts, cold and heat waves adversely affected the Indian foodgrains production to a large extent across the Country in one or other region as seen in 2014, 2012, 2009, 2004, 2002 and 1987. The cold wave in winter 2003- 04 across the Northeast region of the Country and the heat wave in summer 2004 across the same region are still in memory to understand the ill effects in agriculture/horticulture/poultry/inland fisheries sectors. Model simulations
indicate that a marked increase in rainfall and temperature over India would be seen during the current century. The maximum expected increase in rainfall is likely to be 10–30% over the Central India. Temperatures are likely to increase between 2 and 30Cby the end of 2100 A.D. However, steps taken towards the green energy across the world may yield results not to allow atmospheric global temperature beyond 1.5 to 20C. Greater number of high surges and increased occurrence of cyclones in post monsoon period along with increased maximum wind speed are also expected along the East Coast of the Country. The summer droughts and heavy rains leading to waterlogging, floods and landslides during the monsoon period is a threat to plantation crops which are predominantly grown in the Humid Tropics like Kerala (8°15` N and 12°50`N latitudes and between 74°50`E and 77°30`E longitudes).

5.1 Participatory Approaches to Watershed Management

Community Engagement Strategies: Exploring methods for involving local communities in decision-making processes, project planning, and implementation, including participatory rural appraisal (PRA), stakeholder consultations, and community meetings. Exploring methods for involving local communities in decision-making processes, project planning, and implementation is essential for promoting ownership, sustainability, and effectiveness of development initiatives within watershed areas. Here are key community engagement strategies:

a. Participatory Rural Appraisal (PRA): PRA is a participatory approach that involves community members in assessing their own needs, priorities, resources, and capacities. PRA techniques, such as transect walks, social mapping, seasonal calendars, and wealth ranking, enable communities to identify and analyze local issues, strengths, and opportunities. PRA fosters dialogue, builds trust, and empowers communities to take ownership of development processes.

b. Stakeholder Consultations: Conduct stakeholder consultations to engage a diverse range of actors, including community members, local leaders, government officials, civil society organizations, and other stakeholders. Consultations provide opportunities for stakeholders to share their perspectives, knowledge, and concerns, and contribute to decision-making and project design. Stakeholder consultations ensure inclusivity, transparency, and legitimacy in development processes.

c. Community Meetings: Organize community meetings, village assemblies, or public forums to facilitate dialogue, consensus-building, and decision-making among community members. Community meetings provide platforms for discussing development priorities, reviewing project progress, and resolving conflicts collaboratively. They foster social cohesion, collective action, and community mobilization around shared goals and aspirations.

ACID-FORMING FERTILIZER: A term applied to commercial fertilizers which leave an acid residue in the soil. The amount of calcium carbonate required to neutralize the acid residue is referred to its equivalent acidity.

Introduction

Agriculture contributes a vital part within the financial stability of smallest emerging nations such as Pakistan as well. The economic system of Pakistan is fundamentally based upon agriculture (Govt. of Pakistan, 2004-2005). The total surface of Pakistan approximately 796,095 km2, of which 8.3 million km2 are uncultivated and 22 million km2 are farmed (Govt. of Pakistan 2015 2016). Despite an average yearly increase of 2.7%, agriculture contributes 21% of Pakistan’s GDP (Govt of Pakistan, 2004–2005). Farming is a sector whose fundamental and commercial models combine. Agriculture’s importance to the economy may be examined through three distinct perspectives: 1) as a source of nutritious food for the country and raw materials for internal industry; 2) as a way to generate currency from abroad and 3) as a source of products and services for local industries and the global market (Govt. of Pakistan, 2015-16).

The chapter focuses on presenting the outcomes and analysis of the research, aiming to offer ample information for reaching accurate conclusions. Within this chapter, tables and models are frequently employed to reinforce the textual content. The discussion section plays a crucial role in interpreting the research results appropriately and establishing connections with other pertinent studies.

The distribution characteristics of the variable Age (x1 ) reveals that the respondents’ ages range from a minimum of 27 to a maximum of 71. The average age is 48.675, suggesting that the majority of respondents fall into the middle-age category. The standard deviation is 11.574, indicating the degree of variability in the ages. Furthermore, the coefficient of variance (CV) is 23.779 percent, indicating a high level of consistency in the distribution pattern of the variable.

The distribution pattern of the variable Family size (x3 ) shows that the family size ranges from a minimum person of 3 to a maximum of 10 persons. The average family size is 10.125 persons. The standard deviation is 1.397. The coefficient of variance (CV) is 23.882 percent which indicates a high level of consistency in the distribution pattern of the variable.

Introduction

By enhancing microbial populations, enzyme activity, soil respiration, and microbial biomass, nano-black carbon is a novel method for the enhancement of soil biological characteristics. By reducing soil acidity and boosting soil fertility, nano-black carbon is believed to improve plant development. The application of nano-black carbon to agricultural soils can have a significant positive impact on plant productivity. In addition to potentially boosting soil resilience to climate change, nano-black carbon greatly boosted enzyme activity, microbial activity, and soil organic carbon retention.Due to a liming action, nano-black carbon has been proposed as a potential biotechnology to boost agricultural yields in acidic soils.

3.1 INTRODUCTION

Geographic Information System handles large volumes of data. It is important to know the nature of data, instruments from which the data is collected and represented in computers. GIS data is either spatial or aspatial. Data is an important component in GIS. Digital data are available for some projects. but for many projects, input data may not be available. Now-a-days, data is supplied by many government, private organizations and also available on internet. If data for a particular project is not available, then the user has to collect the data from satellite imageries, GPS and other instruments and maps from various sources.

Spatial Data

All locations in the earth surface are spatial data. Location of a city, location of a river, location of a lake are represented in earth by their latitude or longitude in global coordinate system or as x,y coordinate in Cartesian coordinate system. Spatial data consists of location, shape and size in earth. A village boundary is represented as a polygon in map. The village is having certain shape of irregular polygon with definite area. As all these are mappable , they are called as spatial data. But name of the village, revenue of a village and description about a village and called as non-spatial or aspatial data.

The hazardous toxic metals like cadmium (Cd), arsenic (As), mercury (Hg), lead (Pb), and chromium (Cr), which have detrimental health effects, are naturally discharged into the atmosphere in various ways viz., industrial wastes, anthropogenic sources, etc. Heavy metal pollution of the atmosphere has become a frightening ecological concern that has created serious threats to the ecosystem. These metals have been identified as group I, human carcinogens due to their persistence in the surroundings and bio-magnification in biological systems. Thus, the removal of heavy metals from the atmosphere becomes a serious matter from the biological and environmental point of view. In the past following techniques adoptedfor toxic heavy metals remediation were classified are bioadsorption, ion exchange, membrane filtration, flotation, and solvent extraction techniques.

India is recovering from the euphoria of the Green Revolution and battling the residual effects of the extensively used chemical fertilizers and pesticides in the country’s soils. The decade from 1980 to 1990 alone saw the area under pesticides in India, increased a whopping 20-fold, from six million hectares to 125 million hectares. After a high annual consumption of nearly 75000 MT reached in the early ’90s, interventions in the form of Integrated Pest Management (IPM) practices have only now started to show a declining trend in the use of pesticides in India. Interestingly, India’s consumption of pesticides per hectare is low (0.288 kg/ha) when compared with world averages—0.5 kg/ha against Korea’s 6.60 kg/ha and Japan’s 12.0 kg/ha. According to the pesticides industry statistics, India spends US $3/ha on pesticides compared with US $24/ha spent by Philippines, US $255/ha spent by South Korea and US $633/ha by Japan. Yet, despite a comparatively low use of pesticides in India, the contamination of food products in the country is alarming. About 20 per cent of Indian food products contain pesticide residues above tolerance level compared to only 2 per cent globally. No detectable residues are found in only 49 per cent Indian food products compared to 80 per cent globally. A pesticide is any substance or mixture of substances intended for preventing, destroying, repelling or mitigating any pest. Though often misunderstood to refer only to insecticides, the term pesticide also applies to herbicides, fungicides and various other substances used to control pests.

Pesticides and their metabolites have received particular attention in the last few years in environmental trace organic analysis because they are regularly detected in food products, surface/ground waters and soil strata throughout the world as consequence of their widespread use for agricultural and nonagricultural purposes. So it is the need of the hour to study the levels of pesticides residues in plants, water and soil and their fate in environment after its degradation.

It would be necessary to work out tolerance limits of pesticides residues in/on crops, soil and water and the efforts, therefore, are directed towards various residue analytical methods like TLC, GLC and HPLC etc. which are suitable for the purpose.

Climate change and variability are concerns of humankind. The recurrent drought and desertification threaten seriously the livelihood of over 1.2 billion people who depend on land for most of their needs. The global economy has adversely been influenced due to droughts and floods, cold and heat waves, forest fires, landslips and mudslips, icestorms, duststorms, hailstorms, thunder clouds associated with lightning and the sea level rise (Fig. 1). The year 1998 was the warmest and declared as the weather related disaster year, which caused hurricane havoc in Central America and floods in China, India and Bangladesh. Canada and New England in the U.S suffered heavily due to icestorm in January while Turkey, Argentina and Paraguay with floods in June 1998. In contrast, huge crop losses were noticed in Maharashtra (India) due to unseasonal and poor distribution of rainfall during 1997-98. The 1997/1998 El Nino event, the strongest of the last century affected 110 million people and costed the global economy nearly US$ 100 billion. A string of 16 consecutive months saw record high global mean temperature in 1997-98.

8.1 The purpose of erosion research

The need for research

A soil conservation programme needs to be based on factual information about rates and quantities of erosion. For example some land obviously needs to be protected by mechanical works such as channel terraces, some land equally obviously does not need any protection, and a great deal of land lies somewhere between the two extremes. At some point the terraces become unnecessary or uneconomic, but a logical decision on where to draw the line can only be made if data are available on how much soil erosion is occurring and how much it would be reduced by terraces. Similarly, the effect of channel terraces should be compared with the effect of alternative measures, and the effect should also be measured on the different soils and crops which are likely to be encountered.

Nowadays there are excellent opportunities for research workers to exchange the results of their work. The national and iniernational journals are packed with accounts and results of research reports in their proceedings (as listed in Further reading). Some of the reported research results can be usefully applied in countries other than where the research was done, but care is required in any transfer of technology. If the local conditions of soil, climate, or land use, are different, the results may not apply. However, research in other situations can often serve as a pilot trial to show which factors should be studied locally. Even when local conditions are so different that the results do not apply at all, the techniques and apparatus developed by other workers can still be helpful.

The last century has witnessed a global change in agricultural practices. From high dependency on external input agriculture, the world is now shifting towards low external input sustainable agriculture (LEISA). The history of dustbowl devastation in 1930s in America has put agricultural practices, tillage in particular, to question (Friedrich et al., 2012). Traditionally, tillage was believed to be a primary farm operation for preparing a good seedbed, nutrient mobilization, weed management, moisture availability, crop residue incorporation (Hobbs et al., 2008), thereby improving the overall soil health with increased production (Farooq et al., 2011a). The dust bowl devastation of 1930s gave rise to the concept of protecting soil by reducing tillage operation and maintaining ground cover, popularized as conservation tillage (Friedrich et al., 2012). Economic and ecological sufferings caused by the disastrous droughts in USA during 1930s drove the shift towards conservation agriculture (CA) (Haggblade and Tembo, 2003).

Abstract

Carrying out effective and sustainable agriculture has become an important issue in recent years. A key to this is the usage of modern technologies for precision agriculture. Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As more and more agricultural data are virtually being harvested along with the crops and are being collected/ stored in databases, the same can be used in productive decision making if appropriate data mining techniques are developed/applied. As a case study for this, an experiment was conducted with four consecutive agricultural (Kharif and Rabi) seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule and multivariate regression mining techniques/algorithms were propose developed /tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather- environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system.

Keywords : Data Mining, Pest/Disease Management, Precession Protection, Wireless Sensor Networks

India is the seventh largest country in the world with 17 per cent of the world’s population and with a total geographical area of 329 Mha, extending from 8°4’ to 37°6’ in the North and 68°7’ to 97°25’ in the East. It is bounded by the World’s highest mountains- the Himalayas in the North and surrounded by the Arabian Sea, the Indian Ocean and the Bay of Bengal in the South. The Indian Coastline along the southern part of the Country is over 7500 km long. The Country includes vast plains like the Indo-Gangetic Plains, and the Central Deccan Plateau, bordered by the Western Ghats on either side in the West and the South. The biodiversity in the terrain leads to a wide variety of climatic conditions, ranging from tropical to temperate climates across the Western Ghats. These range from permanent snowfields to tropical coastlands; areas of virtual desert in the northwest to fertile intensively cultivated rice fields in the northeast. A large proportion of India’s population continues to live in rural areas and depends heavily on climate-sensitive sectors such as agriculture, fisheries, animal agriculture and forestry for its livelihood. Agriculture is the mainstay of Indian economy. The Indian climate is dominated by the great wind system called the Monsoons, on which the Indian agriculture is dependant. The southwest monsoon is critical to the kharif crop (first crop), which accounts for more than 50% of the foodgrains production and 65% of the oilseeds production in the Country. The inter-annual monsoon rainfall variability in India leads to large-scale droughts and floods, resulting in a major effect on Indian foodgrain production (Parthasarathy and Pant, 1985; Parthasarathy et al., 1992; Selvaraju, 2003; Kumar et al., 2004) and on the economy of the Country (Gadgil et al., 1999a; Kumar and Parikh, 2001). Climatic variability and occurrence of extreme events are major concerns for the Indian subcontinent as they are inbuilt features under the projected climate change scenario. All the projections on climate change are based up on the General Circulation Models (GCMs). All the models project increase in temperature over the Country under different GHG emission scenarios. Rainfall variability is felt in different sub-meteorological divisions across the Country. Though the GCMs have been successful in depicting the gross features of the observed large scale climatological features, there is large uncertainty associated with these projections on regional scale, since the GCMs are yet to realistically reproduce the observed features at regional scale, particularly over the monsoon region (Kumar, et al., 2002). Several downscaling approaches are being used to derive the regional features from large scale model simulations. Under the projected climate change scenario, India will be one of the Countries facing the adverse impacts of climate change as agriculture is the mainstay of people of India. Hence, understanding of rainfall and temperature variability across the Country deserves attention in the projected climate change scenario.